In this episode, we explore what it really takes to build machine learning systems that work reliably in the real world—not just in the lab. While many people think ML ends once a model is trained or when it reaches an impressive accuracy score, the truth is that training is only the beginning. For any mission-critical context—healthcare, finance, infrastructure, public safety—the real work is everything that happens after the model has been created.

We start by reframing ML as an engineering discipline. Instead of focusing solely on algorithms, we look at the full lifecycle of an ML system: design, evaluation, validation, deployment, monitoring, and long-term maintenance. In real-world environments, the safety, reliability, and trustworthiness of a model matter far more than any headline performance metric.

Throughout the episode, we ...